# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" MindVison Classification training script. """

from mindspore import context
from mindspore.context import ParallelMode
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
from mindspore.communication.management import init, get_rank, get_group_size

from mindvision.common.check_param import Validator, Rel
from mindvision.classification.utils import get_config, parse_args
from mindvision.classification.dataset.base_dataset import create_dataset
from mindvision.classification.models.build_train import build_model
from mindvision.classification.models.loss import create_loss
from mindvision.classification.models.optimizer import create_optimizer

def main(pargs):
    # set config context
    config = get_config(pargs.config, overrides=pargs.override)
    context.set_context(mode=context.GRAPH_MODE,
                        device_target=config.device_target,
                        save_graphs=False)

    # run distribute
    if config.run_distribute:
        init()
        context.set_auto_parallel_context(device_num=get_group_size(),
                                          parallel_mode=ParallelMode.DATA_PARALLEL,
                                          gradients_mean=True)
        ckpt_save_dir = config.ckpt_path + "ckpt_" + str(get_rank()) + "/"
    else:
        ckpt_save_dir = config.ckpt_path

    # perpare dataset
    dataset_train = create_dataset(config)
    Validator.check_int(dataset_train.get_dataset_size(), 0, Rel.GT)
    batches_per_epoch = dataset_train.get_dataset_size()

    # set network
    network = build_model(config)

    # set loss, optimizer
    network_loss = create_loss(config)
    network_opt = create_optimizer(network.trainable_params(), config, batches_per_epoch)

    # set checkpoint for the network
    ckpt_config = CheckpointConfig(
        save_checkpoint_steps=config.save_checkpoint_steps,
        keep_checkpoint_max=config.keep_checkpoint_max)
    ckpt_callback = ModelCheckpoint(prefix=config.model_name,
                                    directory=ckpt_save_dir,
                                    config=ckpt_config)

    # init the whole Model
    model = Model(network,
                  network_loss,
                  network_opt,
                  metrics={"Accuracy": Accuracy()})

    # begin to train
    print(f'[Start training `{config.model_name}`]')
    print("="*80)
    model.train(config.epochs,
                dataset_train,
                callbacks=[ckpt_callback, LossMonitor()],
                dataset_sink_mode=config.dataset_sink_mode)
    print(f'[End of training `{config.model_name}`]')


if __name__ == '__main__':
    args = parse_args()
    main(args)
